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Computer Science > Computer Science and Game Theory

Title:Optimal Algorithm for Bayesian Incentive-Compatible

Abstract: We consider a social planner faced with a stream of myopic selfish agents.
The goal of the social planner is to maximize the social welfare, however, it
is limited to using only information asymmetry (regarding previous outcomes)
and cannot use any monetary incentives. The planner recommends actions to
agents, but her recommendations need to be Bayesian Incentive Compatible to be
followed by the agents. Our main result is an optimal algorithm for the
planner, in the case that the actions realizations are deterministic and have a
limited support, making significant important progress on this open problem.
Our optimal protocol has two interesting features. First, it always completes
the exploration of a priori more beneficial actions before exploring a priori
less beneficial actions. Second, the randomization in the protocol is
correlated across agents and actions (and not independent at each decision
time).